Hard-disk drives and digital video compression technologies have created a possibility of time-shifting live television (TV) and recording a large number of TV shows in high quality without having to worry about the availability of tapes or other removable storage media. At the same time, digitalization of audiovisual signals has multiplied the number of content sources for an average user. Huge amounts of video clips are published daily on the Internet across various services, and all major content producers are already making their entire content libraries available online. As a consequence, thousands of potentially increasing programs are made available every day and can be recorded and stored locally for later access. Hence, digitalization of audiovisual materials together with the broad availability of high speed data transfer resulted in an ever increasing amount of content that any consumer has available for consumption at any point in time. Large repositories of audiovisual assets or content items are available on the internet and are competing with digital, live TV channels and video on demand (VoD) libraries, offered by TV service providers. In general, the term “content item” is used here in a sense that it represents an item of information within a content area.
However, in view of this enormous amount of offered content items, individual content selection becomes an important issue. Information that does not fit to a user profile should be filtered out and the right content item that matches a user's needs and preferences (e.g. user profile) should be selected.
Recommender systems address these problems by estimating a degree of likeliness of a certain content item for a certain user profile and automatically ranking the content item. This can be done by comparing a content item's characteristics (e.g. features, metadata, etc.) with a user profile or with similar profiles of other users. Thus, recommender systems can be seen as tools for filtering out unwanted content and bringing interesting content to the attention of the user.
The use of recommender technology is steadily being introduced into the market. Among various examples, websites offer a recommender to support users in finding content items (e.g. movies) they like, and electronics devices (e.g. personal video recorders) use recommender for automatic filtering content items. Recommender systems are increasingly being applied to individualize or personalize services and products by learning a user profile, wherein machine learning techniques can be used to infer the ratings of new content items.
Commonly used recommender techniques are collaborative filtering and naïve Bayesian classification. Thereby, from a vast amount of content items, only those items that match a profile of a user or group of users (i.e. user profile) can be retrieved. Recommenders are typically offered as stand-alone services or units, or as add-ons (e.g. plug-ins) to existing services or units. They increasingly appear in consumer devices, such as TV sets or video recorders, or services used by those devices. Recommenders typically require user feedback to learn a user's preferences. Implicit learning frees the user from having to explicitly rate items, and may by derived by observing user actions such as purchases, downloads, selections of items for play back or deletion, etc. Detected user actions can be interpreted by the recommender and translated into a rating. For example, a recommender may interpret a purchase action as positive rating, or, in case of video items, a total viewing duration of more/less than 50% may imply a positive/negative rating. Typically, a user profile is built by gathering or deriving information from users about what they need and is refined by using the user's preference about the chosen content items.
As the consumers time to select and enjoy the available content assets doesn't increase, the importance of proper guidance towards the most interesting and appropriate audiovisual content gains in importance. Typically internet sites, service providers, and device manufacturers offer isolated solutions to provide proper guidance through their particular offering, e.g. when a particular video is watched, the user is prompted for other videos he/she might like to watch, after the playback of the item has finished. However, there will be no cross-domain suggestions for what he/she might want to watch on other channels, such as live TV for example.
Existing approaches to allow to search across isolated solutions today are based on two elements:
1. Merging of user profiles (here, e.g., two sites agree to share the information gathered about the user in order to improve the targeting capabilities at both sides, taking advantage of the additionally gained insights).
2. Cross domain recommendation (here one user profile is translated into another domain, e.g. the profile derived from the purchasing history of an internet shop is translated into a profile that can be used by a recommender operated as part of a VoD service).
User profiles built up in separated domains or silos show weak cross silo recommendation performance. An EPG (electronic program guide) site that features excellent recommendations for live TV programming will therefore be unable to show the same recommendation performance for assets of a VoD library. This again drives separation, as the owners of the EPG site will not put up VoD recommendations, because bad recommendations, although only for VoD assets, result in an overall decrease in the perception of the quality of the site.